Back to the known - the database!
How big can a data company really get? The answer is gigantic. And that's what we're building.
Over the last few months, I’ve explored several startup ideas, ran many experiments, joined one of India’s top startup residency programs and met hundreds of potential co-founders. As I covered in the last post, there were two huge learnings (full post here):
Cofounder relationships are incredibly important, and serious. It’s essentially something that can easily make or break a startup.
VC math makes it such that a certain type of venture capitalist can only invest in businesses that can become unicorns. I broke down this math and logic in my last post too. Do read it if you missed it, it’s fascinating stuff.
So where do these two lessons mean for the startup I’m building? It means it's all back to the space I know best, with some co-founders I know incredibly well.
I’m building something in the data, information, insights and deep research space! More details soon, as we’re working on a name and settling on the details this week.
I’m also building it with friends I’ve known for a few years, and we’ve worked on projects together in the past. That I’ve realized is quite important to me. (Names and details to follow soon too!)
Data is a space I love, but it’s only recently that I’ve realized how big a company one can build in this space. We all know social media giants like Facebook and companies like Google are in a way data plays - and they’re some of the largest companies in the world - but even companies whose only business is data and intelligence are gigantic!
One of the best known examples, Michael Bloomberg’s gigantic financial data and media platform empire is worth over $50 billion, and does over $10 billion in revenue every year! What’s more, it’s privately held (not listed on the stock markets), and Michael Bloomberg still owns a whopping 88% of it!
But even companies whose only business is providing data feeds of different types to industry are incredibly valuable, and most of us never even hear their names!
Take the case of Resilinc for example. It’s technically a California based company (huge part of the team in Pune, India) that provides supply chain insights and data on supply chain resilience to businesses.
The company has raised $26 Million in VC funding and does at least $10 Million in annual revenue (a minimum estimate).
The place where I got that data, and one of the top sources of private company valuation and VC investment data is a company called Crunchbase.
They’ve raised $106.5 million in funding and did $38 million in revenue in 2021. Lusha, another business to business (B2B) data intelligence platform that gives companies data on other companies that they can sell to has raised $205 million at a $1.5 Billion valuation, hitting unicorn status. Best part, Lusha’s database is largely crowdsourced!
Bottom line, from supply chain data to information on valuations to providing sales leads, there are many interesting and huge companies that are built largely on data, and this is the space that we’re exploring.
The Dirt
In India, data is a super dirty space. It usually means shady operators selling phone numbers so that telemarketers can spam you and try to give you a loan or credit card! It also often means stolen data sold by even shadier entities.
Across the developed world, a lot of interesting and legitimate use cases have been built on data pipelines though, and it’s definitely a very interesting area to explore.
If India’s economy is to grow at anywhere close to the rate most of us hope, and VCs sell to global investors to raise investment capital, then the proper use of data in Indian businesses is an industry that clearly has to get built! We’re exploring many tangents here, but what we know is we’ll be starting small and experimenting.
All of us in the team have alignment on building profitably rather than raising outside capital and burning it for faster growth till we clearly know we have product market fit and need to scale. Current model we’re thinking of working on from this month onwards is to build it as a services and consultancy business, while developing the technology and data streams to scale parallel.
Another HUGE advantage of working in anything related to data - you yourself learn a ton and develop insights people without the data really can’t have. That’s why I loved political data analytics too. In due course, I’ll begin sharing some of the high level insights I get from the data in this newsletter too!
More on everything in the weeks to come!